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Record W2046360483 · doi:10.1086/504951

Parasitic Central Nervous System Infections in Immunocompromised Hosts: Clarification of Malaria Diagnosis

2006· letter· en· W2046360483 on OpenAlexaff
William M. Stauffer, Alan J. Magill, Kevin C. Kain

Bibliographic record

VenueClinical Infectious Diseases · 2006
Typeletter
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersCenter for AIDS Research, University of WashingtonFogarty International CenterNational Institutes of Health
KeywordsMalariaMedicineCentral nervous systemImmunologyProtozoal diseaseIntensive care medicineVirologyInternal medicine

Abstract

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To the Editor—We would like to compliment Walker et al. [1] on their informative review article on CNS parasitic infections in immuncompromised hosts. However, there is a statement and a table regarding the diagnosis of malaria using serological tests and rapid malaria tests that is misleading and deserves clarification. This is particularly important, because the submission package for the first rapid malaria test that may be approved in the United States is nearing completion. Walker et al. state that the “[s]erological tests (ParaSight-F and Immunochromotographic Malaria P[lasmodium] falciparum test) are available, but false-positive test results are common” [1, p. 118]. Table 3 on page 117 of their article makes a similar statement and also indicates that the specimen type for the ParaSight-F (Becton Dickinson) and Immunochromotographic tests is a serum sample. It is important to distinguish serological tests and antigen detection tests and the appropriate specimen type for these assays. Serological assays (for the detection of anti-malarial IgG and IgM in patient serum) have limited clinical utility for the diagnosis of malaria [2]. Most individuals who have resided in areas of moderate or high endemicity for malaria will demonstrate a persistent antibody response. On the other hand, several rapid malaria tests based on the detection of malaria antigens in whole blood samples, such as parasite histidine-rich protein II, aldolase, and lactate dehydrogenase, are available on the global market (however, as far as we know, ParaSight-F is no longer manufactured). These newer-generation assays display high sensitivity and specificity for acute clinical P. falciparum infections, with more variable results for infection due to non-falciparum species of Plasmodium [3–6]. A pivotal clinical trial evaluating the performance of the NOW ICT (Binax) rapid antigen test in >4000 patients was recently presented at the American Society of Tropical Medicine and Hygiene Annual Conference in Washington, D.C., in December 2005 by the US Army [7]. Overall sensitivity and specificity were 95% and 94%, respectively, for P. falciparum infection and 69% and 99%, respectively, for Plasmodium vivax infection. For parasite counts of >5000 parasites/µ L, sensitivity was >99% for P. falciparum infection and 94% for P. vivax infection. In addition, the authors state “false-positive test results are common” [1, p. 117]. In many instances, “false-positives” are seen because the rapid test is more sensitive than routine microscopic examination (with histidine-rich protein II–based tests) or because antigenemia persists for a few days after clearance of parasites. True false-positive test results (i.e., instances in which the rapid test result is positive, but the patient does not have malaria) are much less common in field use. Occasional false-positive results have been seen in patients with a positive rheumatoid factor with some test kits [8]. It is important for infectious disease specialists to understand the appropriate sample type, properties, clinical utility, limitations, and performance characteristics of diagnostic tests for this important disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0250.017
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.331
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2006
Admission routes1
Has abstractno

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